Modeling and uncertainty of complex ground-water systems

A fundamental problem in the analysis of complex ground-water systems is the interplay of data and modeling, both when testing fundamental theories and when calibrating models. Improving how data and models are used has proven to be exceedingly difficult, however, partly because models have been badly misused. One of the most difficult problems is that ground-water model calibration methods used frequently lack any rigorous method of relating models to the calibration data and the predictions of interest, and numerical problems and limitations of forward models exacerbate the problem. Also, the geometry of interconnections and barriers critical to defining ground-water flow are often grossly in error, leading to errors in estimated parameter values, which in turn obscures the ability to derive any general understanding of processes from a set of studies. Also see project home page

Project objectives are to:

Improve forward ground-water modeling capabilities to resolve existing numerical problems and limitations that prohibit accurate simulation of realistic situations. For example, improve the methods used to represent in detail small areas of large-scale models.

Develop inverse modeling methods to make this technology more readily accessible and to resolve remaining difficulties such as the estimation of insensitive parameters.

Investigate constraining models using more sophisticated geologic data and analyses.

Use the insight provided by inverse models to develop data collection techniques, design and interpret laboratory experiments, and evaluate the importance of geologic structures and principles, especially as they interact with microbial processes.

Devise methods for quantifying model uncertainty using both inferential and Monte Carlo approaches.